Fine-Grained Emotion Prediction for Movie and Television scene images

For the task of content retrieval,analysis and generation of film and television scene images in the field of intelligent editing,fine-grained emotion recognition and prediction of images is of great significance.In this paper,the fusion of traditional perceptual features,art features and multi-channel deep learning features are used to reflect the emotion expression of different levels of the image.In addition,the integrated learning model with stacking architecture based on linear regression coefficient and sentiment correlations,which is called the LS-stacking model,is proposed according to the factor association between multi-dimensional emotions.The experimental results prove that the mixed feature and LS-stacking model can predict well on the 16 emotion categories of the self-built image dataset.This study improves the fine-grained recognition ability of image emotion by computers,which helps to increase the intelligence and automation degree of visual retrieval and post-production system.

CNN and Fuzzy Rules Based Text Detection and Recognition from Natural Scenes

In today’s real world, an important research part in image processing isscene text detection and recognition. Scene text can be in different languages,fonts, sizes, colours, orientations and structures. Moreover, the aspect ratios andlayouts of a scene text may differ significantly. All these variations appear assignificant challenges for the detection and recognition algorithms that are consideredfor the text in natural scenes. In this paper, a new intelligent text detection andrecognition method for detectingthe text from natural scenes and forrecognizingthe text by applying the newly proposed Conditional Random Field-based fuzzyrules incorporated Convolutional Neural Network (CR-CNN) has been proposed.Moreover, we have recommended a new text detection method for detecting theexact text from the input natural scene images. For enhancing the presentation ofthe edge detection process, image pre-processing activities such as edge detectionand color modeling have beenapplied in this work. In addition, we have generatednew fuzzy rules for making effective decisions on the processes of text detectionand recognition. The experiments have been directedusing the standard benchmark datasets such as the ICDAR 2003, the ICDAR 2011, the ICDAR2005 and the SVT and have achieved better detection accuracy intext detectionand recognition. By using these three datasets, five different experiments havebeen conducted for evaluating the proposed model. And also, we have comparedthe proposed system with the other classifiers such as the SVM, the MLP and theCNN. In these comparisons, the proposed model has achieved better classificationaccuracywhen compared with the other existing works.

Fast speedometer identification in dynamic scene based on phase correlation

Speedometer identification has been researched for many years.The common approaches to that problem are usually based on image subtraction,which does not adapt to image offsets caused by camera vibration.To cope with the rapidity,robust and accurate requirements of this kind of work in dynamic scene,a fast speedometer identification algorithm is proposed,it utilizes phase correlation method based on regional entire template translation to estimate the offset between images.In order to effectively reduce unnecessary computation and false detection rate,an improved linear Hough transform method with two optimization strategies is presented for pointer line detection.Based on VC＋＋ 6.0 software platform with OpenCV library,the algorithm performance under experiments has shown that it celerity and precision.

Scene matching based on non-linear pre-processing on referenceimage and sensed image

To solve the heterogeneous image scene matching problem, a non-linear pre-processing method for the original images before intensity-based correlation is proposed. The result shows that the proper matching probability is raised greatly. Especially for the low S/N image pairs, the effect is more remarkable.

Embedded System Based Raspberry Pi 4 for Text Detection and Recognition

Detecting and recognizing text from natural scene images presents a challenge because the image quality depends on the conditions in which the image is captured,such as viewing angles,blurring,sensor noise,etc.However,in this paper,a prototype for text detection and recognition from natural scene images is proposed.This prototype is based on the Raspberry Pi 4 and the Universal Serial Bus(USB)camera and embedded our text detection and recognition model,which was developed using the Python language.Our model is based on the deep learning text detector model through the Efficient and Accurate Scene Text Detec-tor(EAST)model for text localization and detection and the Tesseract-OCR,which is used as an Optical Character Recognition(OCR)engine for text recog-nition.Our prototype is controlled by the Virtual Network Computing(VNC)tool through a computer via a wireless connection.The experiment results show that the recognition rate for the captured image through the camera by our prototype can reach 99.75%with low computational complexity.Furthermore,our proto-type is more performant than the Tesseract software in terms of the recognition rate.Besides,it provides the same performance in terms of the recognition rate with a huge decrease in the execution time by an average of 89%compared to the EasyOCR software on the Raspberry Pi 4 board.